Case Study – Signify

How a global connected lighting leader improved API reliability with independent monitoring.

Signify's connected lighting platform depends on APIs that perform consistently across global markets and diverse device ecosystems. When performance issues emerged, Signify needed independent measurement to understand their true scope and location. This case study describes how APIContext helped Signify identify performance problems that internal monitoring had not surfaced, and the improvements that followed from having accurate, geographically distributed visibility into their API estate.

• How independent monitoring surfaced performance issues invisible to internal tools
• The geographic distribution of API performance problems in IoT environments
• How measurement data drove targeted infrastructure improvements
• The reliability improvements that resulted from addressing root-cause performance issues

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# Case Study – Signify

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Description: Learn how Signify used APIContext to identify and address API performance issues, improving reliability for their global connected lighting platform\.

## Summary
Case Study – Signify API Performance Improvements\. How a global connected lighting leader improved API reliability with independent monitoring\.\. Learn how Signify used APIContext to identify and address API performance issues, improving reliability for their global connected lighting platform\.

## Page sections

### How a global connected lighting leader improved API reliability with independent monitoring\.
Category: Case study
Signify's connected lighting platform depends on APIs that perform consistently across global markets and diverse device ecosystems\. When performance issues emerged, Signify needed independent measurement to understand their true scope and location\. This case study describes how APIContext helped Signify identify performance problems that internal monitoring had not surfaced, and the improvements that followed from having accurate, geographically distributed visibility into their API estate\.

- How independent monitoring surfaced performance issues invisible to internal tools
- The geographic distribution of API performance problems in IoT environments
- How measurement data drove targeted infrastructure improvements
- The reliability improvements that resulted from addressing root\-cause performance issues

## Key facts
- Signify's connected lighting platform depends on APIs that perform consistently across global markets and diverse device ecosystems\. When performance issues emerged, Signify needed independent measurement to understand their true scope and location\.
- This case study describes how APIContext helped Signify identify performance problems that internal monitoring had not surfaced, and the improvements that followed from having accurate, geographically distributed visibility into their API estate\.
- How independent monitoring surfaced performance issues invisible to internal tools
- The geographic distribution of API performance problems in IoT environments
- How measurement data drove targeted infrastructure improvements
- The reliability improvements that resulted from addressing root\-cause performance issues

## Primary entities
- APIContext
- Case study
- Case Study – Signify API Performance Improvements
- API monitoring
- API resilience

## Audience
- technology leaders
- API teams
- platform teams
- SRE teams

## Primary links
- [Download the case study](/resources/case-study-signify)